Bibliographic record
Abstract
Abstract Silent disco headphone parties offer an innovative way to foster social connection and engagement among older adults in long-term care (LTC) by allowing residents to dance and enjoy music through wireless, multichannel headphones. Unlike traditional group activities, silent discos provide residents with the autonomy to select their preferred music while participating in a shared, joyful and immersive experience. Given the high prevalence of loneliness and social isolation among LTC residents, finding creative approaches to encourage social interaction and meaningful engagement is essential. This study examined the experiences of 180 participants—including residents, family members, and interdisciplinary staff—across three Canadian LTC homes. Data were collected using video ethnography, incorporating video recordings, conversational interviews, direct observations, and focus groups to capture diverse perspectives on the experience. Thematic analysis revealed three central themes: bridging generations, fostering social interaction, and promoting a sense of community. Participants expressed that silent disco parties created opportunities for spontaneous interactions, strengthened connections between residents and caregivers, and facilitated intergenerational engagement. Findings suggest that silent disco parties have the potential to enhance social well-being in LTC by providing an inclusive, accessible, and adaptable social activity that fosters participation and enjoyment. These events may also contribute to reducing feelings of isolation by promoting movement, shared experiences, and emotional expression through music. Future research should further investigate the long-term benefits of silent disco parties on residents’ social health, emotional well-being, and overall quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".